Since untreated language disorder - a disorder with a prevalence of at least 7% - can lead to serious behavioral and educational problems, large-scale early language assessment is urgently needed not only for early identification of language disorder but also for planning interventions and tracking progress. This is all the more so because a recent study found that 71% of children diagnosed with Specific Language Impairment (a type of language disorder) had not been previously identified. However, such large-scale efforts would pose a large burden on professional staff and on other scarce resources. As a result, clinicians, educators, and researchers have argued for the use of computer based assessment. Recently, progress has been made with computer based language assessment, but it has been limited to language comprehension (i.e., receptive vocabulary and grammar). Thus, computer based assessment of language production that is expressive language and particularly discourse skills, is still lacking. One contributing factor is that a key technology needed for this, Automatic Speech Recognition (ASR), is perceived as inadequate for accurate scoring of language tests since even the best ASR systems have word error rates in excess of 20%. However, this perception is based on a limited perspective of how ASR can be used for assessment, in which a general- purpose ASR system provides an (often inaccurate) transcript of the child's speech, which then would be scored automatically according to conventional rules. We take an alternative perspective, and propose an innovative approach that comprises two core concepts. The first is that of creating special-purpose, test-specific ASR systems whose search space is carefully matched to the space of responses a test may elicit. The second is that of integrating these systems with machine-learning based scoring algorithms whereby the latter operate not on the final, best transcript generated by the ASR system but on the rich layers of intermediate representations that the ASR system computes in the process of recognizing the input speech (rich representation). Earlier experiments in our lab with digit and narrative recall tests have demonstrated the feasibility of this approach. In the proposed project we will create computer-based scoring and test administration systems for tests in the expressive modality as well as in the vocabulary, grammar, and discourse domains; we will also create a system for a non-word repetition test. The systems will be applied to a diverse group of 300 children ages 3-9 with typical development and with neurodevelopmental disorders, and will be validated against conventional language measures. The automated language tests developed in the project cover core diagnostic criteria for language disorders but also create a technological foundation for the computerization of a much broader array of tests for voice based language and cognitive assessment.